Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting.
Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting.
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在资源有限的环境中使用人工智能进行宫颈癌筛查的护理点数字细胞学。
DOI：
10.1001/jamanetworkopen.2021.1740
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发表时间：
2021-03-01
影响因子：
13.8
通讯作者：
Lundin J
中科院分区：
文献类型：
--
作者：
Holmström O;Linder N;Kaingu H;Mbuuko N;Mbete J;Kinyua F;Törnquist S;Muinde M;Krogerus L;Lundin M;Diwan V;Lundin J
这项诊断研究使用来自肯尼亚一家卫生中心的数据，研究在资源有限的地区使用数字显微镜和人工智能来检测巴氏试验中的异常细胞。在资源有限的诊所中，使用基于人工智能的样本评估的即时数字显微镜是否可以在资源有限的环境中实施，其中病理学家的访问权限有限，并用于分析Papanicolaou测试结果？在这项概念验证诊断研究中，收集了来自740名妇女的Papanicolaou测试结果，在肯尼亚的一家农村诊所进行数字化，并使用深度学习算法进行分析，以检测非典型样本。检测乳腺癌的灵敏度很高（96%-100%），高级别病变的特异性（93%-99%）高于低级别病变（82%-86%），手动分类为高级别的载玻片均未被错误分类为阴性。这项研究的结果表明，在人工智能的支持下，先进的数字显微镜诊断在农村资源有限的环境中用于检测巴氏试验中的异常细胞是可行的。宫颈癌是高度可预防的，但在没有筛查计划的地区仍然是一种常见和致命的癌症。创建一个诊断系统来检测Papanicolaou测试样本并使用基于云的深度学习系统（DLS）进行分析，可以为资源有限的地区提供所需的宫颈癌筛查。确定人工智能支持的数字显微镜诊断是否可以在资源有限的环境中实施，并用于巴氏试验的分析。在这项诊断研究中，在2018年9月1日至2019年9月30日期间收集了740名年龄在18至64岁之间的艾滋病毒阳性妇女的宫颈涂片。涂片用便携式载玻片扫描仪数字化，使用移动的网络上传到云服务器，并用于训练和验证用于检测非典型宫颈细胞的DLS。这项单中心研究是在肯尼亚农村的一个当地卫生保健中心进行的。通过DLS分析检测数字样本中的鳞状细胞异型性。DLS在巴氏试验全载玻片图像中检测低度和高度鳞状上皮内病变的准确性。收集了740名HIV阳性女性（平均[SD]年龄，41.8 [10.3]岁）的巴氏试验结果。使用350张全切片图像对DLS进行训练，并在361张全切片图像（平均大小，100 387 × 47 560像素）上进行验证。对宫颈细胞增生的敏感性为95.7%（95% CI，85.5%-99.5%）和100%特异性为84.7%（95%CI为82.4%~ 100%）（95% CI，80.2%-88.5%）和78.4%（95% CI，73.6%-82.4%），与病理学家对数字和物理切片的评估相比。受试者工作特征曲线下面积分别为0.94和0.96。阴性预测值高（99%-100%），准确性高，特别是对于高级别病变的检测。与病理学家对数字载玻片的评估（κ = 0.72）相比，评价者间一致性显著，与载玻片评估（κ = 0.36）相比，评价者间一致性一般。通过手动样本分析被归类为高等级的样本均未通过DLS进行假阴性评估。在这项研究中，人工智能数字显微镜在农村诊所实施，并用于检测非典型宫颈涂片，与视觉样本分析相比具有高灵敏度。
This diagnostic study uses data from a single health center in Kenya to investigate the use of digital microscopy and artificial intelligence in a resource-limited area to detect abnormal cells in Papanicolaou tests. Can point-of-care digital microscopy with artificial intelligence–based sample assessment be implemented at a clinic in a resource-limited setting where access to pathologists is limited and used to analyze Papanicolaou test results? In this proof-of-concept diagnostic study, Papanicolaou test results from 740 women were collected, digitized at a rural clinic in Kenya, and analyzed with a deep learning algorithm to detect atypical samples. The sensitivity for detection of atypia was high (96%-100%), with higher specificity for high-grade lesions (93%-99%) than for low-grade lesions (82%-86%), and no slides manually classified as high grade were incorrectly classified as negative. The results of this study suggest that advanced digital microscopy diagnostics, supported by artificial intelligence, are feasible to use in rural, resource-limited settings for detection of abnormal cells in Papanicolaou tests. Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagnostic system to digitize Papanicolaou test samples and analyze them using a cloud-based deep learning system (DLS) may provide needed cervical cancer screening to resource-limited areas. To determine whether artificial intelligence–supported digital microscopy diagnostics can be implemented in a resource-limited setting and used for analysis of Papanicolaou tests. In this diagnostic study, cervical smears from 740 HIV-positive women aged between 18 and 64 years were collected between September 1, 2018, and September 30, 2019. The smears were digitized with a portable slide scanner, uploaded to a cloud server using mobile networks, and used to train and validate a DLS for the detection of atypical cervical cells. This single-center study was conducted at a local health care center in rural Kenya. Detection of squamous cell atypia in the digital samples by analysis with the DLS. The accuracy of the DLS in the detection of low- and high-grade squamous intraepithelial lesions in Papanicolaou test whole-slide images. Papanicolaou test results from 740 HIV-positive women (mean [SD] age, 41.8 [10.3] years) were collected. The DLS was trained using 350 whole-slide images and validated on 361 whole-slide images (average size, 100 387 × 47 560 pixels). For detection of cervical cellular atypia, sensitivities were 95.7% (95% CI, 85.5%-99.5%) and 100% (95% CI, 82.4%-100%), and specificities were 84.7% (95% CI, 80.2%-88.5%) and 78.4% (95% CI, 73.6%-82.4%), compared with the pathologist assessment of digital and physical slides, respectively. Areas under the receiver operating characteristic curve were 0.94 and 0.96, respectively. Negative predictive values were high (99%-100%), and accuracy was high, particularly for the detection of high-grade lesions. Interrater agreement was substantial compared with the pathologist assessment of digital slides (κ = 0.72) and fair compared with the assessment of glass slides (κ = 0.36). No samples that were classified as high grade by manual sample analysis had false-negative assessments by the DLS. In this study, digital microscopy with artificial intelligence was implemented at a rural clinic and used to detect atypical cervical smears with a high sensitivity compared with visual sample analysis.
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影响因子：
34.3
作者：
Arbyn, Marc;Weiderpass, Elisabete;Bray, Freddie
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Bray, Freddie
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3.1
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51.1
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Katki, Hormuzd A.;Kinney, Walter K.;Fetterman, Barbara;Lorey, Thomas;Poitras, Nancy E.;Cheung, Li;Demuth, Franklin;Schiffman, Mark;Wacholder, Sholom;Castle, Philip E.
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Castle, Philip E.
影响因子：
64.8
作者：
Esteva A;Kuprel B;Novoa RA;Ko J;Swetter SM;Blau HM;Thrun S
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Thrun S